Elsevier

Addictive Behaviors

Volume 114, March 2021, 106751
Addictive Behaviors

The relationship between delay discounting and Internet addiction: A systematic review and meta-analysis

https://doi.org/10.1016/j.addbeh.2020.106751Get rights and content

Highlights

  • Delay discounting can be used as an appropriate outcome to measure impulsivity in individuals with Internet addiction.

  • Judicious use of analysis methods is recommended.

  • The modes of administration need to be concerned.

  • The male predominance and task-related variables have significant impacts on discounting rate.

Abstract

Aims

To estimate the difference in delay discounting (DD) between subjects with Internet addiction (IA) and those without as well as to identify significant variables involved in DD.

Methods

Using the keywords related to IA (e.g., “excessive Internet use”, “Internet dependence”) AND “delayed reward discounting” OR “delay discounting” OR “temporal discounting” OR “delayed gratification” OR time discounting OR intertemporal choice OR impulsive choice, the PubMed, Embase, and PsycINFO databases were searched from inception to June 2020 for English articles with comparison between subjects with IA and those without. Effect sizes were calculated by group means from the k value or area under the curve (AUC). The random-effects models were used.

Results

Fourteen studies in total were eligible for the current meta-analysis that involved 696 subjects with IA (mean age = 22.71) and 2,394 subjects without (mean age = 21.91). Subjects with IA had a steeper DD rate (g = 1.10, 95% CI: 0.57–1.64; p ≤ 0.01) compared with that in those without. Regarding DD data, the difference between k value and AUC was significant (p < 0.01; AUC > k). Additionally, the estimation of DD by the paper-and-pencil task was larger than that by the computerized task (p < 0.01). Significant difference in the DD rate was also noted between subjects with Internet gaming disorder (IGD) and those with unspecified IA (p = 0.00; IGD > IA). The percentage of men and task variables were significantly associated with the DD rate (all p < 0.01), suggesting impaired DD in subjects with IA.

Conclusions

Our results suggested the feasibility of utilizing the DD rate as a therapeutic index for cognitive control in IA. Nevertheless, judicious use is recommended taking into consideration the significant difference between k value and AUC.

Introduction

Among different behavioral measures of impulse control, delay discounting (DD) task is often used for evaluating the capacity to tolerate delay in reward (Dougherty, Mathias, Marsh, & Jagar, 2005). The DD tasks consist of a series of choices between immediately available smaller rewards and greater rewards available only after some length of time (Matta, Gonçalves, & Bizarro, 2012). Preferring immediate rewards to potentially more satisfying experience later is considered to indicate a poor impulse control (Hoffman et al., 2008, Monterosso et al., 2007). In addition to the DD task, existing literature also employs the delayed gratification (DG) task to assess one’s ability to withstand a delay in reward; however, Reynolds and Schiffbauer (2005) demonstrated that the processes involved in the DD task required a higher level of cognitive function and more learning-mediated ability than those required by the DG task. Thus, the DG task is more suitable for young children, while the DD task is used preferably for adolescents and adults (Göllner, Ballhausen, Kliegel, & Forstmeier, 2018). Actually, DD is more often used to measure impulse control in different preclinical and clinical groups (Amlung et al., 2019, Matta et al., 2012), such as subjects with attention-deficit/hyperactivity disorder (ADHD) (Wilson, Mitchell, Musser, Schmitt, & Nigg, 2011), schizophrenia (Brown et al., 2018, Heerey et al., 2007), bipolar disorder (Urošević, Youngstrom, Collins, Jensen, & Luciana, 2016), obesity (Appelhans et al., 2011, Weller et al., 2008) as well as gambling (Dixon, Marley, & Jacobs, 2003) and addiction (Robles et al., 2011, Saville et al., 2010). Furthermore, several meta-analyses were conducted to give reliable proof of a steeper discounting in individuals with ADHD (Weicker, Villringer, & Thöne-Otto, 2016), obesity (Amlung, Petker, Jackson, Balodis, & MacKillop, 2016), gambling (MacKillop et al., 2014) and addiction (Amlung et al., 2017, MacKillop et al., 2011) compared to those without. Recently, Amlung et al. (2019) preformed a meta-analysis and showed that DD could be used as a transdiagnostic criterion across many mental disorders.

Although DD can be assessed by using a variety of techniques (Madden & Johnson, 2010), they are conducted on a hypothetical and experiential basis. Indeed, hypothetical rewards and delays are involved in the majority of human DD research (Odum, 2011). Moreover, even using real reward, a previous investigation did not detect a significant correlation between reward type (real and hypothetical money) and DD (Madden, Begotka, Raiff, & Kastern, 2003). Subjects indifferently chose two alternatives (small but immediate reward vs. larger but delayed reward), namely the indifference points, across a series of intertemporal choices, expressed as DD function. Two mathematical models are used to explain the discounting curve (Myerson & Green, 1995), including the exponential (Lancaster, 1963, Meyer, 1976) and hyperbolic equations (de Villiers and Herrnstein, 1976, Mazur, 1987). The hyperbolic equation can provide a good fit for individuals’ preferences according to existing research evidence (Madden et al., 2003). Mazur (1987) was the first to adopt a rate parameter from a hyperbolic function to quantify DD data:Vd=1/(1+kD)where Vd is the subjective value of delayed gain, D is the delay, and k is the discounting parameter (Koffarnus et al., 2017, Mazur, 1987). The higher the k value, the steeper the decrease in the subjective value of delayed gain (see Fig. 1). The reverse is also true. Therefore, k value is regarded as the impulsivity index. It is the specific DD rate for an individual participant determined by the transition of this curve from asymptotic values near 1.0 at lower delays to asymptotic values near 0.0 at higher delays. In addition to k value, the area under the curve (AUC) is used to analyzed DD. Myerson, Green, and Warusawitharana (2001) suggested the use of AUC, instead of k value, to measure DD for two main reasons: (1) the distribution of area measures, unlike distributions of estimates of the parameters, is not skewed; (2) unlike measures based on the parameters of a discounting function, the area measure requires no assumptions regarding the mathematical form of this function (Myerson et al., 2001). In contrast to k value, the AUC score as impulsivity reverses from 0 (no delay) to 1 (maximum delay) (Myerson et al., 2001). In other words, larger AUCs represent stronger inhibition for immediate reward (i.e., less discounting by delay), that is, subject are less impulsive (or more self-controlled) (Odum, 2011).

Regarding addiction, DD is further regarded as an endophenotype (MacKillop, 2013) that is potentially useful to identify the influenced attribute of a disorder-predisposing genotype (Gottesman & Gould, 2003). Although two previous meta-analyses have shown that individuals with substance addiction had poorer performance on the DD task (i.e., steeper discounting of delayed rewards) (Amlung et al., 2017, MacKillop et al., 2011), findings from subjects with substance addiction may not be extrapolated to those with IA as evidence suggests that IA, a behavioral addiction, involves a long period of fluctuating cognitive control (LaRose et al., 2003, Polivy, 1998) rather than neuropharmacological mechanisms per se (Kauer & Malenka, 2007). In other words, IA is more closely associated with the cognitive process that allows the individual to compare values between the immediate and delayed rewards (i.e., DD task) (Loewenstein, 1988, Matta et al., 2012). However, the relationship between DD and IA has not been systematically reviewed and analyzed.

At least five types of IA are identified, such as Internet gaming addiction (Kuss & Griffiths, 2012), cybersex (Delmonico, 1997), online shopping addiction (Rose & Dhandayudham, 2014), social networking (media) addiction (Griffiths, Kuss, & Demetrovics, 2014), and Internet gambling addiction (Griffiths & Parke, 2008). The previous study by Kuss & Griffiths (2012) supported the idea that Internet gaming addiction can be regarded as an addictive disorder rather than an impulse-control disorder. Following a provisional criterion for Internet gaming disorder (IGD) in the fifth edition of Diagnostic and Statistical Manual of Mental Disorders (DSM-5) (Petry and O'Brien, 2013, Petry et al., 2015), gaming disorder was officially adopted at the World Health Assembly in May 2019 as a diagnosis in the eleventh edition of the International Classification of Diseases (ICD-11; World Health Organization, 2019). A distinctive diagnosis of IGD suggests that it may differ from the other types of IA (Griffiths et al., 2016, Pontes and Griffiths, 2014). Existing studies also found that subjects with different types of IA showed their respective characteristics, such as gender and psychological factors (Collins et al., 2012, Hong et al., 2012, Kircaburun et al., 2020, Park et al., 2007, Ryan et al., 2014, Wang et al., 2015). The debate about addictions on the Internet versus addictions to the Internet is still ongoing (Billieux, 2012, Griffiths et al., 2016). DD is used to estimate the impulsivity across the types of IA, such as Internet gaming (Tian et al., 2018), gambling (Stieg & Dixon, 2007), online shopping (Hantula, Brockman, & Smith, 2008), and cybersex (Negash, Sheppard, Lambert, & Fincham, 2016). However, whether subjects with different types of IA have similar DD may need to be clarified.

The primary purpose of this meta-analysis was to investigate the difference in DD between subjects with IA and those without. Additionally, we looked for the significant variables which influenced DD (e.g., data analysis methods, modes of administration, and gender).

Section snippets

Study eligibility and definitions

Addictive behavior is regarded as compulsive use, dependence, overuse, and abuse. Besides “problematic” or “pathological” Internet use (Griffiths et al., 2016), a wide range of nomenclature across the psychological, psychiatric, and neuroscientific literature has been used to refer to IA (Pontes, Kuss, and Griffiths (2015). Therefore, different terms were used to ensure the completeness of literature search for the current study, namely, Internet addiction OR problematic Internet use OR

Study characteristics

The data from 14 studies were included in the current meta-analysis. Participants included 696 subjects of IA (mean age = 22.71 years) and 2,394 healthy subjects (mean age = 21.91 years). The characteristics of the selected studies are listed in Table 1. Subjects’ age was over 18 years in most studies except the study by Tian et al. (2018).There was a male predominance as expected. Young’s diagnostic questionnaire for Internet addiction (YDQ) and Young’s Internet addiction test (YIAT) were the

Main findings

The present study had several notable findings. First, the DD rate in the IA group was steeper than that in its comparators, indicating that subjects with IA had greater impulsivity. Second, because of the significant difference in results between k value and AUC function in discounting rate analysis, judicious use of the two methods is suggested. Third, the studies conducted by the paper-and-pencil task showed a steeper DD rate compared to that in those using computerized administration.

Conclusion

The current study demonstrated a significant difference in DD between subjects with IA and those without. Judicious use of analysis methods for representing DD is suggested because of a significant difference in results when different methods were used. The modes of administration may actually influence the performance of DD. The comparisons between subjects with different types of IA in the DD rate were worth further exploration. The significant correlation between the DD rate and task

CRediT authorship contribution statement

Yu-Shian Cheng: Data curation, Investigation, Project administration, Writing - original draft. Huei-Chen Ko: Supervision, Writing - review & editing. Cheuk-Kwan Sun: Writing - review & editing. Pin-Yang Yeh: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Writing - original draft.

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